Modeling and insights into the structural characteristics of endocrine-disrupting chemicals.

Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the past few decades, but the reported models are not always easily available, and few studies have investigated the structural characteristics of EDCs. In the present study, we have developed a series of artificial intelligence models targeting EDC receptors: the androgen receptor (AR); estrogen receptor (ER); and pregnane X receptor (PXR). The consensus models achieved good predictive results for validation sets with balanced accuracy values of 87.37%, 90.13%, and 79.21% for AR, ER, and PXR binding assays, respectively. Analysis of the physical-chemical properties suggested that several chemical properties were significantly (p < 0.05) different between EDCs and non-EDCs. We also identified structural alerts that can indicate an EDC, which were integrated into the web server SApredictor. These models and structural characteristics can provide useful tools and information in the discrimination and mechanistic understanding of EDCs in drug discovery and environmental risk assessment.

Comparative Effectiveness and Safety of Milrinone and Levosimendan as Initial Inotrope Therapy in Patients With Acute Heart Failure With Renal Dysfunction.

ABSTRACT: Levosimendan and milrinone are 2 effective inotropic drugs used to maintain cardiac output in acute heart failure (AHF). Using data from patients with AHF with and without abnormal renal function, we performed this single-center, retrospective cohort study to compare the effectiveness and safety of milrinone and levosimendan for the initial management of AHF. Patients admitted for heart failure between December 2016 and September 2019 who received levosimendan or milrinone as initial inotrope therapy in the cardiology department were identified. A total of 436 levosimendan and 417 milrinone patients with creatinine clearance (CrCl) ≥30 mL/min and 50 levosimendan and 71 milrinone patients with CrCl <30 mL/min or on dialysis were included. The primary outcome was a composite of changes in clinical status at 15 and 30 days after initial inotrope therapy discontinuation. Between subgroups of patients with CrCl ≥30 mL/min, there were no significant differences in primary outcomes; milrinone was associated with more frequent hypotension and cardiac arrhythmias during the infusion period (P < 0.01), while levosimendan was associated with more frequent cardiac arrhythmias within 48 hours after discontinuation (P < 0.05). Of the patients with CrCl <30 mL/min or on dialysis, more initial levosimendan than milrinone patients and those who switched to alternative inotropes experienced clinical worsening at 15 days and 30 days (P < 0.05). According to our results, patients with AHF with severe renal dysfunction should avoid initial inotrope therapy with levosimendan.

Machine Learning Modeling and Insights into the Structural Characteristics of Drug-Induced Neurotoxicity.

Neurotoxicity can be resulted from many diverse clinical drugs, which has been a cause of concern to human populations across the world. The detection of drug-induced neurotoxicity (DINeurot) potential with biological experimental methods always required a lot of budget and time. In addition, few studies have addressed the structural characteristics of neurotoxic chemicals. In this study, we focused on the computational modeling for drug-induced neurotoxicity with machine learning methods and the insights into the structural characteristics of neurotoxic chemicals. Based on the clinical drug data with neurotoxicity effects, we developed 35 different classifiers by combining five different machine learning methods and seven fingerprint packages. The best-performing model achieved good results on both 5-fold cross-validation (balanced accuracy of 76.51%, AUC value of 0.83, and MCC value of 0.52) and external validation (balanced accuracy of 83.63%, AUC value of 0.87, and MCC value of 0.67). The model can be freely accessed on the web server DINeuroTpredictor (http://dineurot.sapredictor.cn/). We also analyzed the distribution of several key molecular properties between neurotoxic and non-neurotoxic structures. The results indicated that several physicochemical properties were significantly different between the neurotoxic and non-neurotoxic compounds, including molecular polar surface area (MPSA), AlogP, the number of hydrogen bond acceptors (nHAcc) and donors (nHDon), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). In addition, 18 structural alerts responsible for chemical neurotoxicity were identified. The structural alerts have been integrated with our web server SApredictor (http://www.sapredictor.cn). The results of this study could provide useful information for the understanding of the structural characteristics and computational prediction for chemical neurotoxicity.

Modeling and insights into the structural basis of chemical acute aquatic toxicity.

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).

A competitive immunoassay for detecting triazophos based on fluorescent catalytic hairpin self-assembly.

A rapid detection method is introduced for residual trace levels of triazophos in water and agricultural products using an immunoassay based on catalytic hairpin self-assembly (CHA). The gold nanoparticle (AuNPs) surface was modified with triazophos antibody and sulfhydryl bio-barcode, and an immune competition reaction system was established between triazophos and its ovalbumin-hapten (OVA-hapten). The bio-barcode served as a catalyst to continuously induce the CHA reaction to achieve the dual signal amplification. The method does not rely on the participation of enzymes, and the addition of fluorescent materials in the last step avoids interfering factors, such as a fluorescence burst. The emitted fluorescence was detected at 489/521 nm excitation/emission wavelengths. The detection range of the developed method was 0.01-50 ng/mL for triazophos, and the limit of detection (LOD) was 0.0048 ng/mL. The developed method correlates well with the results obtained by LC-MS/MS, with satisfactory recovery and sensitivity. In sum, the designed method is reliable and provides a new approach to detect pesticide residues rapidly and quantitatively.

In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.

Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment.

Modeling and insights into molecular basis of low molecular weight respiratory sensitizers.

Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.

Regulation of las and rhl Quorum Sensing on Aerobic Denitrification in Pseudomonas aeruginosa PAO1.

The bacterium Pseudomonas aeruginosa negatively regulates denitrification under anerobic conditions by two acyl-homoserine lactone quorum-sensing (QS) systems called las and rhl. However, it is unknown whether these systems have the same effect on denitrification in aerobic conditions. In this study, we investigated the regulation of las and rhl systems on aerobic denitrification. We showed that the removal of nitrate in P. aeruginosa PAO1 was repressed by both the las and rhl systems. The las and rhl systems had negative effects on activities of denitrifying enzymes NAP, NIR, NOR, and NOS. At the level of transcription, both QS systems inhibited the expression of target genes napA, nirS, norB, norC, and nosZ. Furthermore, the addition of an acylase, which degrades the acyl-homoserine lactone signals (AHLs), to wild type resulted in an increase in the removal of nitrate. Additionally, in aerobic denitrification process, the transcription factor DNR, which controls denitrification, was repressed by both QS systems. The results implied that modulation of QS in denitrifying bacteria, possibly through quorum quenching or QS inhibition, could help to improve the reduction of nitrate in wastewater treatment.

Bioaugmentation and quorum sensing disruption as solutions to increase nitrate removal in sequencing batch reactors treating nitrate-rich wastewater.

Bioaugmentation of denitrifying bacteria can serve as a promising technique to improve nutrient removal during wastewater treatment. While denitrification inhibition by bacterial quorum sensing (QS) in Pseudomonas aeruginosa has been indicated, the application of bacterial QS disruption to improve nitrate removal from wastewater has not been investigated. In this study, the effect of bioaugmentation of P. aeruginosa SD-1 on nitrate removal in sequencing batch reactors that treat nitrate rich wastewater was assessed. Additionally, the potential of a quorum sensing inhibitor (QSI) to improve denitrification following bacterial bioaugmentation was evaluated. Curcumin, a natural plant extract, was used as a QSI. The chemical oxygen demand (COD) and initial nitrate concentration of the influent were 700±20 mg/L and 200±10 mg/L respectively, and their respective concentrations in the effluent were 56.9±3.2 mg/L and 9.0±3.2 mg/L. Thus, the results revealed that bioaugmentation of P. aeruginosa SD-1 resulted in an increased nitrate removal to 82%±1%. Further, nitrate was almost completely removed following the addition of the QSI, and activities of nitrate reductase and nitrite reductase increased by 88%±2% and 74%±2% respectively. The nitrogen mass balance indicated that aerobic denitrification was employed as the main pathway for nitrogen removal in the reactors. The results imply that bioaugmentation and modulation of QS in denitrifying bacteria, through the use of a QSI, can enhance nitrate removal during wastewater treatment.

In silico prediction of drug-induced rhabdomyolysis with machine-learning models and structural alerts.

Drug-induced rhabdomyolysis (DIR) is a serious adverse reaction and can be fatal. In the present study, we focused on the modeling and understanding of the molecular basis of DIR of small molecule drugs. A series of machine-learning models were developed using an Online Chemical Modeling Environment platform with a diverse dataset. A total of 80 machine-learning models were generated. Based on the top-performing individual models, a consensus model was also developed. The consensus model was available at https://ochem.eu/model/32214665, and the individual models can be accessed with the corresponding model IDs on the website. Furthermore, we also analyzed the difference of distributions of eight key physicochemical properties between rhabdomyolysis-inducing drugs and non-rhabdomyolysis-inducing drugs. Finally, structural alerts responsible for DIR were identified from fragments of the Klekota-Roth fingerprints.

Colorimetric bio-barcode immunoassay for parathion based on amplification by using platinum nanoparticles acting as a nanozyme.

A competitive bio-barcode immunoassay is described for the trace detection of parathion in water, pear, cabbage, and rice samples. It is based on amplification by platinum nanoparticle acting as a nanozyme. Gold nanoparticles (AuNPs) were modified with (a) monoclonal antibodies (mAbs) against parathion, and (b) thiolated single-stranded DNA (ssDNA) oligonucleotides. Magnetic nanoparticles (MNPs) were functionalized with ovalbumin coupled with parathion hapten. Parathion and its hapten compete with mAbs on the surface of the AuNPs. Subsequently, the platinum nanoparticles (PtNPs) probe, which was functionalized with the complementary thiolated ssDNA (C-ssDNA), was added to the reaction mixture for the detection of parathion. The signal was catalytically amplified by coupling with platinum nanozyme using teramethylbenzidine and H2O2 as the chromogenic system. The immunoassay has a linear range that extends from 0.01-50 µg·L-1, and the limit of detection is 2.0 × 10-3 µg·L-1. The recoveries and relative standard deviations (RSDs) ranged from 91.1-114.4% and 3.6-15.8%, respectively. The method correlates well with data obtained by gas chromatography-tandem mass spectrometry (GC-MS/MS). Graphical abstract The parathion and the magnetic nanoparticles (MNPs) labelled with hapten-OVA competitively reacted to AuNPs modified with mAbs and thiolated DNA for the detection of parathion. The signal was catalyzed by platinum nanozyme. The limit of detection for parathion is 2.0 ng·L-1.

A sensitive chemiluminescence enzyme immunoassay based on molecularly imprinted polymers solid-phase extraction of parathion.

The chemiluminescence enzyme immunoassay (CLEIA) method responds differently to various sample matrices because of the matrix effect. In this work, the CLEIA method was coupled with molecularly imprinted polymers (MIPs) synthesized by precipitation polymerization to study the matrix effect. The sample recoveries ranged from 72.62% to 121.89%, with a relative standard deviation (RSD) of 3.74-18.14%.The ratio of the sample matrix-matched standard curve slope rate to the solvent standard curve slope was 1.21, 1.12, 1.17, and 0.85 for apple, rice, orange and cabbage in samples pretreated with the mixture of PSA and C18. However, the ratio of sample (apple, rice, orange, and cabbage) matrix-matched standard-MIPs curve slope rate to the solvent standard curve was 1.05, 0.92, 1.09, and 1.05 in samples pretreated with MIPs, respectively. The results demonstrated that the matrices of the samples greatly interfered with the detection of parathion residues by CLEIA. The MIPs bound specifically to the parathion in the samples and eliminated the matrix interference effect. Therefore, the CLEIA method have successfully applied MIPs in sample pretreatment to eliminate matrix interference effects and provided a new sensitive assay for agro-products.

"Watching" the Dark State in Ultrafast Nonadiabatic Photoisomerization Process of a Light-Driven Molecular Rotary Motor.

Photoisomerization dynamics of a light-driven molecular rotary motor, 9-(2-methyl-2,3-dihydro-1H-cyclopenta[a]naphthalen-1-ylidene)-9H-fluorene, is investigated with trajectory surface-hopping dynamics at the semiempirical OM2/MRCI level. The rapid population decay of the S1 excited state for the M isomer is observed, with two different decay time scales (500 fs and 1.0 ps). By weighting the contributions of fast and slow decay trajectories, the averaged lifetime of the S1 excited state is about 710 fs. The calculated quantum yield of the M-to-P photoisomerization of this molecular rotary motor is about 59.9%. After the S0 → S1 excitation, the dynamical process of electronic decay is followed by twisting about the central C═C double bond and the motion of pyramidalization at the carbon atom of the stator-axle linkage. Although two S0/S1 minimum-energy conical intersections are obtained at the OM2/MRCI level, only one conical intersection is found to be responsible for the nonadiabatic dynamics. The existence of "dark state" in the molecular rotary motor is confirmed through the simulated time-resolved fluorescence emission spectrum. Both quenching and red shift of fluorescence emission spectrum observed by Conyard et al. [ Conyard, J.; Addison, K.; Heisler, I. A.; Cnossen, A.; Browne, W. R.; Feringa, B. L.; Meech, S. R. Nat. Chem. 2012 , 4 , 547 - 551 ; Conyard, J.; Conssen, A.; Browne, W. R.; Feringa, B. L.; Meech, S. R. J. Am. Chem. Soc. 2014 , 136 , 9692 - 9700 ] are well understood. We find that this "dark state" in the molecular rotary motor is not a new electronic state, but the "dark region" with low oscillator strength on the initial S1 state.

Development of Magnetic Lateral Flow and Direct Competitive Immunoassays for Sensitive and Specific Detection of Halosulfuron-Methyl Using a Novel Hapten and Monoclonal Antibody.

Halosulfuron-methyl (HM) is widely used for the removal of noxious weeds in corn, sugarcane, wheat, rice, and tomato fields. Despite its high efficiency and low toxicity, drift to nontarget crops and leaching of its metabolites to groundwater pose potential risks. Considering the instability of HM, the pyrazole sulfonamide of HM was used to generate a hapten and antigen to raise a high-quality monoclonal antibody (Mab, designated 1A91H11) against HM. A direct competitive immunoassay (dcELISA) using Mab 1A91H11 achieved a half-maximal inhibitory concentration (IC50) of 1.5 × 10-3 mg/kg and a linear range of 0.7 × 10-3 mg/kg-10.7 × 10-3 mg/kg, which was 10 times more sensitive than a comparable indirect competitive ELISA (icELISA) and more simple to operate. A spiking recovery experiment performed in tomato and maize matrices with 0.01, 0.05, and 0.1 mg/kg HM had average recoveries within 78.9-87.9% and 103.0-107.4% and coefficients of variation from 1.1-6.8% and 2.7-6.4% in tomato and maize, respectively. In addition, a magnetic lateral flow immunoassay (MLFIA) was developed for quantitative detection of low concentrations of HM in paddy water. Compared with dcELISA, the MLFIA exhibited 3.3- to 50-fold higher sensitivity (IC50 0.21 × 10-3 mg/kg). The average recovery and RSD of the developed MLFIA ranged from 81.5 to 92.5% and 5.4 to 9.7%. The results of this study demonstrated that the developed dcELISA and MLFIA are suitable for rapid detection of HM residues in tomato and maize matrices and paddy water, respectively, with acceptable accuracy and precision.

A Smartphone-Integrated Molecularly Imprinted Fluorescence Sensor for Visual Detection of Chlortetracycline Based on N,P-Codoped Carbon Dots Decorated Iron-Based Metal-Organic Frameworks.

The residue of chlortetracycline is potentially hazardous to human health; it is meaningful to exploit a portable, rapid, sensitive, and selective method for detection of chlortetracycline (CTC). In this study, a novel fluorescence bionic sensing probe (NH2-MIL-53&N,P-CDs@MIP) was successfully prepared based on the nitrogen and phosphorus codoped carbon dots decorated iron-based metal-organic frameworks combining with molecular imprinted polymer for the detection of CTC. A fluorescence intensity-responsive "on-off" detection of CTC on account of the inner-filter effect (IFE) was achieved by NH2-MIL-53&N,P-CDs@MIP. Under the optimal conditions, the fluorescence quenching degree of NH2-MIL-53&N,P-CDs@MIP presented a good linear relationship with the CTC concentration in the range 0.06-30 µg mL-1 and the limit of detection (LOD) was 0.019 µg mL-1. The fluorescent probe was applied to detect CTC in milk samples, and experimental results showed a good recovery rate (88.73%-96.28%). Additionally, a smartphone-integrated fluorescence sensing device based on NH2-MIL-53&N,P-CDs@MIP was exploited to replace the expensive and bulky fluorescence spectrophotometer for quantitative determination of CTC with the LOD of 0.033 µg mL-1. The sensing system showed high selectivity, strong stability, high specificity, and portability, which provide a great strategy for the quantitative detection of antibiotic residue.

The participation of clinical pharmacists in the treatment of patients with central nervous system infection can improve the effectiveness and appropriateness of anti-infective treatments: a retrospective cohort study.

Background: Central nervous system infection (CNSI) treatment in hospital neurosurgery emphasizes the importance of optimizing antimicrobial therapy. Timely and appropriate empiric antibiotic treatment is critical for managing patients with bacterial meningitis. Objectives: To evaluate the activities of clinical pharmacists in the anti-infective treatment of patients with CNSI in neurosurgery. Method: A single-center retrospective cohort study was carried out from January 2021 to March 2023 at a tertiary teaching hospital in China. The study sample included a group that received pharmacy services and a group that did not. In the pharmacy services group, the anti-infective treatment plan was led and developed by pharmacists. Pharmaceutical care, including medication therapy and all CNSI treatment regimens, was administered in daily unit rounds by pharmacists. Baseline demographics, treatment outcomes, and rational use of antibiotics were compared between the two groups, and the impact of a antimicrobial stewardship (AMS) program was evaluated. Results: Of the 306 patients assessed according to the inclusion and exclusion criteria, 151 patients were included, and 155 patients were excluded due to abnormal data and missing information on antibiotic costs or antimicrobial use. Eventually, 73 were included in the pharmacy services group and 78 in the group without pharmacist participation. The antibiotic use density (AUD) of the pharmacy services group decreased from 167.68 to 127.63 compared to the group without pharmacist participation. After the pharmacist services, the AUD for linezolid decreased from 9.15% to 5.23% and that for miscellaneous agents decreased from 17.91% to 6.72%. The pharmacy services group had better improvement (p < 0.05) and a significantly higher score for the rational use of antibiotics (p < 0.05) than the group without pharmacist participation. Conclusion: The clinical pharmacist services evaluation results demonstrated an essential role of clinical pharmacist-led AMS programs in the effective and appropriate use of anti-infective treatments in neurosurgery with patients with CNSI.

Competitive fluorescent immunosensor based on catalytic hairpin self-assembly for multiresidue detection of organophosphate pesticides in agricultural products.

Simple and rapid multiresidue trace detection of organophosphate pesticides (OPs) is extremely important for various reasons, including food safety, environmental monitoring, and national health. Here, a catalytic hairpin self-assembly (CHA)-based competitive fluorescent immunosensor was developed to detect OPs in agricultural products, involving enabled dual signal amplification followed by a CHA reaction. The developed method could detect 0.01-50 ng/mL triazophos, parathion, and chlorpyrifos, with limits of detection (LODs) of 0.012, 0.0057, and 0.0074 ng/mL, respectively. The spiked recoveries of samples measured using this assay ranged from 82.8 % to 110.6 %, with CV values ranging between 5.5 % and 18.5 %. This finding suggests that the CHA-based competitive fluorescent immunosensor is a reliable and accurate method for detecting OPs in agricultural products. The results correlated well with those obtained from the liquid chromatography-tandem mass spectrometry (LC-MS/MS) method, indicating that the CHA-based biosensor is able to accurately detect OPs and can be used as a reliable alternative to the LC-MS/MS method. Additionally, the CHA-based biosensor is simpler and faster than LC-MS/MS, which makes it a more practical and cost-effective option for the detection of OPs. In summary, the CHA-based competitive fluorescent immunosensor can be considered a promising approach for trace analysis and multiresidue determination of pesticides, which can open up new horizons in the fields of food safety, environmental monitoring, and national health.

SARS-CoV-2 vaccines are not associated with hypercoagulability in apparently healthy people.

Background: SARS-CoV-2 adenoviral vector DNA vaccines have been linked to the rare but serious thrombotic postvaccine complication vaccine-induced immune thrombotic thrombocytopenia. This has raised concerns regarding the possibility of increased thrombotic risk after any SARS-CoV-2 vaccines. Objectives: To investigate whether SARS-CoV-2 vaccines cause coagulation activation leading to a hypercoagulable state. Methods: This observational study included 567 health care personnel; 521 were recruited after the first dose of adenoviral vector ChAdOx1-S (Vaxzevria, AstraZeneca) vaccine and 46 were recruited prospectively before vaccination with a messenger RNA (mRNA) vaccine, either Spikevax (Moderna, n = 38) or Comirnaty (Pfizer-BioNTech, n = 8). In the mRNA group, samples were acquired before and 1 to 2 weeks after vaccination. In addition to the prevaccination samples, 56 unvaccinated blood donors were recruited as controls (total n = 102). Thrombin generation, D-dimer levels, and free tissue factor pathway inhibitor (TFPI) levels were analyzed. Results: No participant experienced thrombosis, vaccine-induced immune thrombotic thrombocytopenia, or thrombocytopenia (platelet count <100 × 109/L) 1 week to 1 month postvaccination. There was no increase in thrombin generation, D-dimer level, or TFPI level in the ChAdOx1-S vaccine group compared with controls or after the mRNA vaccines compared with baseline values. Eleven of 513 (2.1%) participants vaccinated with ChAdOx1-S had anti-PF4/polyanion antibodies without a concomitant increase in thrombin generation. Conclusion: In this study, SARS-CoV-2 vaccines were not associated with thrombosis, thrombocytopenia, increased thrombin generation, D-dimer levels, or TFPI levels compared with baseline or unvaccinated controls. These findings argue against the subclinical activation of coagulation post-COVID-19 vaccination.

Safety of butylphthalide and edaravone in patients with ischemic stroke: a multicenter real-world study.

BACKGROUND: Butylphthalide (NBP) and edaravone (EDV) injection are common acute ischemic stroke medications in China, but there is a lack of large real-world safety studies on them. This study aimed to determine the incidence of adverse events, detect relevant safety signals, and assess the risk factors associated with these medications in real-world populations. METHODS: In this study, data of acute ischemic stroke patients were extracted from the electronic medical record database of six tertiary hospitals between January 2019 and August 2021. Baseline confounders were eliminated using propensity score matching. The drugs' safety was estimated by comparing the results of 24 laboratory tests standards on liver function, kidney function, lipid level, and coagulation function. The drugs' relative risk was estimated by logistic regression. A third group with patients who did not receive NBP or EDV was constructed as a reference. Prescription sequence symmetry analysis was used to evaluate the associations between adverse events and NBP and EDV, respectively. RESULTS: 81,292 patients were included in this study. After propensity score matching, the NBP, EDV, and third groups with 727 patients in each group. Among the 15 test items, the incidence of adverse events was lower in the NBP group than in the EDV group, and the differences were statistically significant. The multivariate logistic regression equation revealed that NBP injection was not a promoting factor for abnormal laboratory test results, whereas EDV had statistically significant effects on aspartate transaminase, low-density lipoprotein cholesterol and total cholesterol. Prescription sequence symmetry analysis showed that NBP had a weak correlation with abnormal platelet count. EDV had a positive signal associated with abnormal results in gamma-glutamyl transferase, alanine aminotransferase, aspartate aminotransferase, prothrombin time, and platelet count. CONCLUSIONS: In a large real-world population, NBP has a lower incidence of adverse events and a better safety profile than EDV or other usual medications.